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The variability of the channel and environment is one of the most important factors affecting the performance of text-independent speaker verification systems. The best techniques for channel compensation are model based. Most of them have been proposed for Gaussian Mixture Models, while in the feature domain blind channel compensation is usually performed. The aim of this work is to explore techniques that allow more accurate intersession compensation in the feature domain. Compensating the features rather than the models has the advantage that the transformed parameters can be used with models of a different nature and complexity, and for different tasks. In this paper, we evaluate the effects of the compensation of the intersession variability obtained by means of the channel factors approach. In particular, we compare channel variability modeling in the usual Gaussian Mixture model domain, and our proposed feature domain compensation technique. We show that the two approaches lead to similar results on the NIST 2005 Speaker Recognition Evaluation data with a reduced computation cost. We report also the results of a system, based on the intersession compensation technique in the feature space that was among the best participants in the NIST 2006 Speaker Recognition Evaluation. Moreover, we show how we obtained significant performance improvement in language recognition by estimating and compensating, in the feature domain, the distortions due to inter-speaker variability within the same language.

The variability of the channel and environment is one of the most important factors affecting the performance of textindependent speaker verification systems.The best techniques for channel compensation are model based. Most of them have been proposed for Gaussian Mixture Models, while in the feature domain typically blind channel compensation is performed.The aim of this work is to explore techniques that allow more accurate channel compensation in the domain of the features. Compensating the features rather than the models has the advantage that the transformed parameters can be used with models of different nature and complexity, and also for different tasks.In this paper we evaluate the effects of the compensation of the channel variability obtained by means of the channel factors approach. In particular, we compare channel variability modeling in the usual Gaussian Mixture model domain, and our proposed feature domain compensation technique. We show that the two approaches lead to similar results on the NIST 2005 Speaker Recognition Evaluation data.Moreover, the quality of the transformed features is also assessed in the Support Vector Machines framework for speaker recognition on the same data, and in preliminary experiments on Language Identification.

This paper describes the experimental setup and the results obtained using several state-of-the-art speaker recognition classifiers. The comparison of the different approaches aims at the development of real world applications, taking into account memory and computational constraints, and possible mismatches with respect to the training environment. The NIST SRE 2008 database has been considered our reference dataset, whereas nine commercially available databases of conversational speech in languages different form the ones used for developing the speaker recognition systems have been tested as representative of an application domain. Our results, evaluated on the two domains, show that the classifiers based on i-vectors obtain the best recognition and calibration accuracy. Gaussian PLDA and a recently introduced discriminative SVM together with an adaptive symmetric score normalization achieve the best performance using low memory and processing resources.

This paper describes the Nuance-Politecnico di Torino (NPT) speaker recognition system submitted to the NIST SRE12 evaluation campaign. Included are the results of postevaluation tests, focusing on the analysis of the effects of score normalization and condition-dependent calibration. The submitted system combines the results of five acoustic recognizers all based on Gaussian Mixture Models (GMMs). Each system has its own front end, with features differing by their type and dimension. We illustrate the process of development data selection and configuration of state-of-the art technology, which contributed to obtaining good performance in all the test conditions proposed in this evaluation.